Methods exist to identify target objects based on certain images obtained by image capturing means such as a camera, including methods in which identification is performed by using feature quantities related to shapes and the like, and methods in which identification is performed by using color information. Methods in which identification is performed by using color information are robust with respect to direction of target objects and image-capturing angle, and are instinctively easy to understand, and thus useful.
On the other hand, a problem with methods in which identification is performed by using color information is that color information observed (perceived) in each image changes depending upon individual characteristics of each camera, lighting environment, and the like.
For example, with surveillance which employs a wide-area surveillance system, in some cases multiple cameras are installed in order to survey a wide area. In such cases, lighting environments differ greatly depending on camera installation location, and there is thus a possibility that, even if target objects having the same color are being captured, the objects might be perceived to have different colors. In addition, if cameras having different color characteristics are to be used, it is advisable to do color calibration on the cameras beforehand in order not to make differences in color perception between cameras. However, performing color calibration on all cameras beforehand is difficult, and there is thus a possibility that, even if the same object is captured under the same environments of lighting conditions, the object may be observed as having differing color information.
In addition, even if images are captured by a single camera, in outdoor environments in particular the lighting environment differs greatly depending on the time of the day, and thus the observed color information differs as well.
In order to solve these problems, such color correction technology is necessary that corrects color information obtained from images to stable color information which does not differ depending upon color characteristics of each camera and lighting environments.
The gray world hypothesis is disclosed in NPL 1. In addition, the gray edge hypothesis is disclosed in NPL 2. By utilization of these technologies, lighting colors for a scene (space that is an image capturing target) as a whole can be estimated. In addition, based on the estimated lighting colors, color balance for the scene as a whole can be corrected.
In addition, technology which reconciles the appearance of target objects that moves between multiple cameras is disclosed in NPL 3. With this technology, color correction is performed by defining and calculating the respective brightness transfer functions (BTFs). A BTF is a function which associates a brightness distribution of an image observed by one camera with a brightness distribution of an image observed by another camera. A BTF between two cameras is learned by utilization of color changes of a target object moving between the cameras. A variety of representations exist for BTFs, and gamma correction formulas and histogram distances are types of BTFs.
In this way, by learning BTF between cameras, color correction can be performed such that color information for the same target object can be stably obtained by and among multiple cameras.
In addition, in PTL1, such a visual examination method is disclosed that, even when a state of surfaces of objects to be examined have individual differences, can remove these individual differences and examine the surfaces of the objects with a high degree of accuracy.
In addition, in PTL 2, an image recognition device is disclosed which can perform image recognition with a high degree of accuracy.
Moreover, in PTL 3, such an image processing device is disclosed that can adaptively enhance the quality of moving images automatically with no flickering, regardless of the video source and regardless of the scene.